Figure 8. Ratio of the maximum value of the peaks measured by the sensors against the maximum value of the peaks measured by the DustTrak before and after calibration for each experiment. The box and whisker plot horizontal lines represent, from bottom to top, the lower quartile, the median and the upper quartile. The vertical lines are drawn to the smallest and the largest data point that fall within 1.5 times the interquartile range below the lower quartile and above the upper quartile respectively. Values outside these range are considered as outliers and plotted as points.


source("utilities.R")
source("variables.R")
require(ggplot2)
require(plotly)
require(cowplot)
require(splitstackshape)
require(ggpubr)
source("Table_2_rh_exploration.R")


df <- prepare_sensor_data(sensor_delay_corrected, cdt)
dt <- prepare_dusttrak_data(dusttrak_file, cdt)

df %>%
  select(sensor, exp, source, date, PM25) %>%
  filter(sensor!="SHT35") %>%
  filter(exp!="") %>%
  group_by(exp, source, sensor) %>%
  nest() -> tmp

tmp %>%
  dplyr::mutate(regression = map(data, ~clean_regression(df =.x,dusttrak = dt, 0))) %>%
  unnest(regression, .drop=TRUE)  ->res



res<-cSplit(indt=res,splitCols = c("sensor"),sep="-",direction="wide",drop=FALSE) %>%
  select(-sensor_2,-sensor_3)->res

res <- res %>%
  flag_site_data(sensor_list)

res_rh_temp <- inner_join(res, summary_rh_temp, by = c("exp"))


#write.csv(select(res_rh_temp,-data), "C:/GitHub/AQ_analysis_lab/lm_no_enclosure_cdt_corr.csv")

res_rh_temp <- res_rh_temp %>%
  mutate(rh_median  = round2(rh_median,0))
df %>% inner_join(select(res_rh_temp, sensor, exp, source, slope, intercept, slope_p_value), by = c("sensor", "exp", "source")) -> df_coeff

df_coeff %>% 
  mutate(PM25_corr = ifelse(slope_p_value<=0.05,(PM25-intercept)/slope), -999) %>%
  filter(PM25_corr != -999) -> df_calibrated

df_calibrated<-cSplit(indt=df_calibrated,splitCols = c("sensor"),sep="-",direction="wide",drop=FALSE) %>%
  select(-sensor_2,-sensor_3)

What does it looks like?

p_opcr1 <- ggplot()+
  geom_line(data = filter(df_calibrated, sensor_1=="OPCR1", exp != ""),
            aes(x = date, y = PM25_corr, group = sensor, colour = sensor, 
                text = paste0("Box: ", site,
                              "<br>Exp: ", exp,
                              "<br>Source: ", source))) +
  labs(y = "PM2.5 (ug/m3)") +
  geom_line(data = filter(dt, exp != ""), aes(x = date,y = pm2.5), colour = "red", linetype = "dashed")+
  scale_x_datetime() + theme_bw() + ggtitle("OPCR1") + theme(plot.title = element_text(hjust = 0.5))+facet_wrap(~exp, ncol = 1, scales = "free_x")
Ignoring unknown aesthetics: text
p_pms5003 <- ggplot()+
  geom_line(data = filter(df_calibrated, sensor_1=="PMS5003", exp != ""),
            aes(x = date, y = PM25_corr, group = sensor, colour = sensor, 
                text = paste0("Box: ", site,
                              "<br>Exp: ", exp,
                              "<br>Source: ", source))) +
  labs(y = "PM2.5 (ug/m3)") +
  geom_line(data = filter(dt, exp != ""), aes(x = date,y = pm2.5), colour = "red", linetype = "dashed")+
  scale_x_datetime() + theme_bw() + ggtitle("PMS5003") + theme(plot.title = element_text(hjust = 0.5))+facet_wrap(~exp, ncol = 1, scales = "free_x")
Ignoring unknown aesthetics: text
p_sds018 <- ggplot()+
  geom_line(data = filter(df_calibrated, sensor_1=="SDS018", exp != ""),
            aes(x = date, y = PM25_corr, group = sensor, colour = sensor, 
                text = paste0("Box: ", site,
                              "<br>Exp: ", exp,
                              "<br>Source: ", source))) +
  labs(y = "PM2.5 (ug/m3)") +
  geom_line(data = filter(dt, exp != ""), aes(x = date,y = pm2.5), colour = "red", linetype = "dashed")+
  scale_x_datetime() + theme_bw() + ggtitle("SDS018") + theme(plot.title = element_text(hjust = 0.5))+facet_wrap(~exp, ncol = 1, scales = "free_x")
Ignoring unknown aesthetics: text
p_sps030 <- ggplot()+
  geom_line(data = filter(df_calibrated, sensor_1=="SPS030", exp != ""),
            aes(x = date, y = PM25_corr, group = sensor, colour = sensor, 
                text = paste0("Box: ", site,
                              "<br>Exp: ", exp,
                              "<br>Source: ", source))) +
  labs(y = "PM2.5 (ug/m3)") +
  geom_line(data = filter(dt, exp != ""), aes(x = date,y = pm2.5), colour = "red", linetype = "dashed")+
  scale_x_datetime() + theme_bw() + ggtitle("SPS030") + theme(plot.title = element_text(hjust = 0.5))+facet_wrap(~exp, ncol = 1, scales = "free_x")
Ignoring unknown aesthetics: text
p_hpma <- ggplot()+
  geom_line(data = filter(df_calibrated, sensor_1=="HPMA115S0", exp != ""),
            aes(x = date, y = PM25_corr, group = sensor, colour = sensor, 
                text = paste0("Box: ", site,
                              "<br>Exp: ", exp,
                              "<br>Source: ", source))) +
  labs(y = "PM2.5 (ug/m3)") +
  geom_line(data = filter(dt, exp != ""), aes(x = date,y = pm2.5), colour = "red", linetype = "dashed")+
  scale_x_datetime() + theme_bw() + ggtitle("HPMA115S0") + theme(plot.title = element_text(hjust = 0.5))+facet_wrap(~exp, ncol = 1, scales = "free_x")
Ignoring unknown aesthetics: text
ggplotly(p_opcr1, dynamicTicks = TRUE)

ggplotly(p_pms5003, dynamicTicks = TRUE)

ggplotly(p_sds018, dynamicTicks = TRUE)

ggplotly(p_sps030, dynamicTicks = TRUE)

ggplotly(p_hpma, dynamicTicks = TRUE)

Now we extract the peaks.

peaks<-readRDS(file="datasets/peaks_characteristics.rds") 


res <- df_calibrated %>%
    filter(date>=as.POSIXct(paste("2019-09-05", peaks[1,]$Start),tz="UTC"), date<=as.POSIXct(paste("2019-09-05", peaks[1,]$End),tz="UTC")) %>%
    group_by(sensor) %>%
  filter(PM25>=1) %>%
  summarise(max_value = max(PM25_corr)) %>%
    mutate(Experiment = peaks[1,]$Experiment, Source = peaks[1,]$Source, Variation= peaks[1,]$Variation, Number = peaks[1,]$Number)
for(row in 2:nrow(peaks)){
  df_calibrated %>%
    filter(date>=as.POSIXct(paste("2019-09-05", peaks[row,]$Start),tz="UTC"), date<=as.POSIXct(paste("2019-09-05", peaks[row,]$End),tz="UTC")) %>%
    group_by(sensor) %>%
      filter(PM25>=1) %>%
  summarise(max_value = max(PM25_corr)) %>%
    mutate(Experiment = peaks[row,]$Experiment, Source = peaks[row,]$Source, Variation= peaks[row,]$Variation, Number = peaks[row,]$Number) -> tmp
  res<-rbind(res,tmp)
  
} 
res %>%
  inner_join(peaks, by=c("Source", "Experiment", "Variation", "Number")) ->res_bind

res_bind<-res_bind %>%
  mutate(ratio = max_value/Concentration)



df2<-cSplit(indt=res_bind,splitCols = c("sensor"),sep="-",direction="wide",drop=FALSE)

df2 %>%
  select(-sensor_2,-sensor_3)->res_bin

Compare with uncalibrated data


res <- df %>%
  filter(date >= as.POSIXct(paste("2019-09-05", peaks[1,]$Start), tz = "UTC"), 
         date <= as.POSIXct(paste("2019-09-05", peaks[1,]$End), tz = "UTC")) %>%
  group_by(sensor) %>%
  filter(PM25 >= 1) %>%
  summarise(max_value = max(PM25)) %>%
  mutate(Experiment = peaks[1, ]$Experiment, Source = peaks[1, ]$Source, Variation = peaks[1, ]$Variation, Number = peaks[1, ]$Number)
for(row in 2:nrow(peaks)){
  df %>%
    filter(date >= as.POSIXct(paste("2019-09-05", peaks[row, ]$Start), tz = "UTC"), 
           date <= as.POSIXct(paste("2019-09-05", peaks[row, ]$End), tz = "UTC")) %>%
    group_by(sensor) %>%
    filter(PM25 >= 1) %>%
  summarise(max_value = max(PM25)) %>%
  mutate(Experiment = peaks[row, ]$Experiment, Source = peaks[row, ]$Source, 
         Variation= peaks[row, ]$Variation, Number = peaks[row, ]$Number) -> tmp
  res<-rbind(res,tmp)
  
} 
res_bind_uncalibrated<- res %>%
  inner_join(peaks, by = c("Source", "Experiment", "Variation", "Number"))  %>%
  mutate(ratio = max_value/Concentration)


res_bind$status <- "Calibrated"
res_bind_uncalibrated$status <- "Not Calibrated"
res_comparison <- rbind(res_bind, res_bind_uncalibrated)

df2<-cSplit(indt=res_comparison,splitCols = c("sensor"),sep="-",direction="wide",drop=FALSE)

df2 %>%
  select(-sensor_2,-sensor_3)->res_comparison


res_comparison$sensor_1 <- factor(res_comparison$sensor_1, levels = c("PMS5003", "SPS030", "HPMA115S0", "SDS018", "OPCR1"))

res_comparison$status <- factor(res_comparison$status, levels = c("Not Calibrated", "Calibrated"))
res_comparison %>%
  filter(Variation == "Peak") %>%
  ggplot(aes(x=Experiment,y=ratio,fill=status)) + 
   geom_boxplot(width=0.8,position=position_dodge(width = 0.8),alpha=0.8,lwd=0.3,fatten=0.8,outlier.size = 0.8,outlier.alpha=0.5,outlier.stroke=0)+
  facet_wrap(sensor_1~Source,ncol=4)+ylab("Ratio sensor/dusttrak")+theme_bw()+
  scale_y_continuous(breaks=pretty(c(-1,6), n=14),sec.axis = dup_axis(name=NULL))+
  theme(axis.text.x = element_text(angle=90,size = 6))->p
p_legend<-get_legend(p)

plot_grid(p_legend)


p<-p+theme(legend.position = "none")
p<-p+  theme(panel.spacing = unit(0, "lines")) +
  theme(plot.margin = unit(c(0,0,0,0), "lines"))
p
ggsave(p,filename = "ratio_comparison_spacing.svg",unit = "mm", width= 180, height = 240)
ggsave(as_ggplot(p_legend),filename = "ratio_comparison_legend.svg",unit = "mm", width= 180, height = 240)

NA
NA
---
title: "Figure 8 - Calibration of the sensors"
output: html_notebook
editor_options: 
  chunk_output_type: inline
---

Figure 8. Ratio of the maximum value of the peaks measured by the sensors against the maximum
value of the peaks measured by the DustTrak before and after calibration for each experiment. The box
and whisker plot horizontal lines represent, from bottom to top, the lower quartile, the median and the
upper quartile. The vertical lines are drawn to the smallest and the largest data point that fall within
1.5 times the interquartile range below the lower quartile and above the upper quartile respectively.
Values outside these range are considered as outliers and plotted as points.

```{r setup}

source("utilities.R")
source("variables.R")
require(ggplot2)
require(plotly)
require(cowplot)
require(splitstackshape)
require(ggpubr)
source("Table_2_rh_exploration.R")


```


```{r}

df <- prepare_sensor_data(sensor_delay_corrected, cdt)
dt <- prepare_dusttrak_data(dusttrak_file, cdt)

df %>%
  select(sensor, exp, source, date, PM25) %>%
  filter(sensor!="SHT35") %>%
  filter(exp!="") %>%
  group_by(exp, source, sensor) %>%
  nest() -> tmp

tmp %>%
  dplyr::mutate(regression = map(data, ~clean_regression(df =.x,dusttrak = dt, 0))) %>%
  unnest(regression, .drop=TRUE)  ->res



res<-cSplit(indt=res,splitCols = c("sensor"),sep="-",direction="wide",drop=FALSE) %>%
  select(-sensor_2,-sensor_3)->res

res <- res %>%
  flag_site_data(sensor_list)

res_rh_temp <- inner_join(res, summary_rh_temp, by = c("exp"))


#write.csv(select(res_rh_temp,-data), "C:/GitHub/AQ_analysis_lab/lm_no_enclosure_cdt_corr.csv")

res_rh_temp <- res_rh_temp %>%
  mutate(rh_median  = round2(rh_median,0))
```


```{r}
df %>% inner_join(select(res_rh_temp, sensor, exp, source, slope, intercept, slope_p_value), by = c("sensor", "exp", "source")) -> df_coeff

df_coeff %>% 
  mutate(PM25_corr = ifelse(slope_p_value<=0.05,(PM25-intercept)/slope), -999) %>%
  filter(PM25_corr != -999) -> df_calibrated

df_calibrated<-cSplit(indt=df_calibrated,splitCols = c("sensor"),sep="-",direction="wide",drop=FALSE) %>%
  select(-sensor_2,-sensor_3)
```

What does it looks like?

```{r}
p_opcr1 <- ggplot()+
  geom_line(data = filter(df_calibrated, sensor_1=="OPCR1", exp != ""),
            aes(x = date, y = PM25_corr, group = sensor, colour = sensor, 
                text = paste0("Box: ", site,
                              "<br>Exp: ", exp,
                              "<br>Source: ", source))) +
  labs(y = "PM2.5 (ug/m3)") +
  geom_line(data = filter(dt, exp != ""), aes(x = date,y = pm2.5), colour = "red", linetype = "dashed")+
  scale_x_datetime() + theme_bw() + ggtitle("OPCR1") + theme(plot.title = element_text(hjust = 0.5))+facet_wrap(~exp, ncol = 1, scales = "free_x")


p_pms5003 <- ggplot()+
  geom_line(data = filter(df_calibrated, sensor_1=="PMS5003", exp != ""),
            aes(x = date, y = PM25_corr, group = sensor, colour = sensor, 
                text = paste0("Box: ", site,
                              "<br>Exp: ", exp,
                              "<br>Source: ", source))) +
  labs(y = "PM2.5 (ug/m3)") +
  geom_line(data = filter(dt, exp != ""), aes(x = date,y = pm2.5), colour = "red", linetype = "dashed")+
  scale_x_datetime() + theme_bw() + ggtitle("PMS5003") + theme(plot.title = element_text(hjust = 0.5))+facet_wrap(~exp, ncol = 1, scales = "free_x")

p_sds018 <- ggplot()+
  geom_line(data = filter(df_calibrated, sensor_1=="SDS018", exp != ""),
            aes(x = date, y = PM25_corr, group = sensor, colour = sensor, 
                text = paste0("Box: ", site,
                              "<br>Exp: ", exp,
                              "<br>Source: ", source))) +
  labs(y = "PM2.5 (ug/m3)") +
  geom_line(data = filter(dt, exp != ""), aes(x = date,y = pm2.5), colour = "red", linetype = "dashed")+
  scale_x_datetime() + theme_bw() + ggtitle("SDS018") + theme(plot.title = element_text(hjust = 0.5))+facet_wrap(~exp, ncol = 1, scales = "free_x")

p_sps030 <- ggplot()+
  geom_line(data = filter(df_calibrated, sensor_1=="SPS030", exp != ""),
            aes(x = date, y = PM25_corr, group = sensor, colour = sensor, 
                text = paste0("Box: ", site,
                              "<br>Exp: ", exp,
                              "<br>Source: ", source))) +
  labs(y = "PM2.5 (ug/m3)") +
  geom_line(data = filter(dt, exp != ""), aes(x = date,y = pm2.5), colour = "red", linetype = "dashed")+
  scale_x_datetime() + theme_bw() + ggtitle("SPS030") + theme(plot.title = element_text(hjust = 0.5))+facet_wrap(~exp, ncol = 1, scales = "free_x")

p_hpma <- ggplot()+
  geom_line(data = filter(df_calibrated, sensor_1=="HPMA115S0", exp != ""),
            aes(x = date, y = PM25_corr, group = sensor, colour = sensor, 
                text = paste0("Box: ", site,
                              "<br>Exp: ", exp,
                              "<br>Source: ", source))) +
  labs(y = "PM2.5 (ug/m3)") +
  geom_line(data = filter(dt, exp != ""), aes(x = date,y = pm2.5), colour = "red", linetype = "dashed")+
  scale_x_datetime() + theme_bw() + ggtitle("HPMA115S0") + theme(plot.title = element_text(hjust = 0.5))+facet_wrap(~exp, ncol = 1, scales = "free_x")

ggplotly(p_opcr1, dynamicTicks = TRUE)
ggplotly(p_pms5003, dynamicTicks = TRUE)
ggplotly(p_sds018, dynamicTicks = TRUE)
ggplotly(p_sps030, dynamicTicks = TRUE)
ggplotly(p_hpma, dynamicTicks = TRUE)
```

Now we extract the peaks.

```{r}
peaks<-readRDS(file="datasets/peaks_characteristics.rds") 


res <- df_calibrated %>%
    filter(date>=as.POSIXct(paste("2019-09-05", peaks[1,]$Start),tz="UTC"), date<=as.POSIXct(paste("2019-09-05", peaks[1,]$End),tz="UTC")) %>%
    group_by(sensor) %>%
  filter(PM25>=1) %>%
  summarise(max_value = max(PM25_corr)) %>%
    mutate(Experiment = peaks[1,]$Experiment, Source = peaks[1,]$Source, Variation= peaks[1,]$Variation, Number = peaks[1,]$Number)
for(row in 2:nrow(peaks)){
  df_calibrated %>%
    filter(date>=as.POSIXct(paste("2019-09-05", peaks[row,]$Start),tz="UTC"), date<=as.POSIXct(paste("2019-09-05", peaks[row,]$End),tz="UTC")) %>%
    group_by(sensor) %>%
      filter(PM25>=1) %>%
  summarise(max_value = max(PM25_corr)) %>%
    mutate(Experiment = peaks[row,]$Experiment, Source = peaks[row,]$Source, Variation= peaks[row,]$Variation, Number = peaks[row,]$Number) -> tmp
  res<-rbind(res,tmp)
  
} 
res %>%
  inner_join(peaks, by=c("Source", "Experiment", "Variation", "Number")) ->res_bind

res_bind<-res_bind %>%
  mutate(ratio = max_value/Concentration)



res_bin<-cSplit(indt=res_bind,splitCols = c("sensor"),sep="-",direction="wide",drop=FALSE) %>%
  select(-sensor_2,-sensor_3)


```


# Compare with uncalibrated data

```{r}

res <- df %>%
  filter(date >= as.POSIXct(paste("2019-09-05", peaks[1,]$Start), tz = "UTC"), 
         date <= as.POSIXct(paste("2019-09-05", peaks[1,]$End), tz = "UTC")) %>%
  group_by(sensor) %>%
  filter(PM25 >= 1) %>%
  summarise(max_value = max(PM25)) %>%
  mutate(Experiment = peaks[1, ]$Experiment, Source = peaks[1, ]$Source, Variation = peaks[1, ]$Variation, Number = peaks[1, ]$Number)
for(row in 2:nrow(peaks)){
  df %>%
    filter(date >= as.POSIXct(paste("2019-09-05", peaks[row, ]$Start), tz = "UTC"), 
           date <= as.POSIXct(paste("2019-09-05", peaks[row, ]$End), tz = "UTC")) %>%
    group_by(sensor) %>%
    filter(PM25 >= 1) %>%
  summarise(max_value = max(PM25)) %>%
  mutate(Experiment = peaks[row, ]$Experiment, Source = peaks[row, ]$Source, 
         Variation= peaks[row, ]$Variation, Number = peaks[row, ]$Number) -> tmp
  res<-rbind(res,tmp)
  
} 
res_bind_uncalibrated<- res %>%
  inner_join(peaks, by = c("Source", "Experiment", "Variation", "Number"))  %>%
  mutate(ratio = max_value/Concentration)


res_bind$status <- "Calibrated"
res_bind_uncalibrated$status <- "Not Calibrated"
res_comparison <- rbind(res_bind, res_bind_uncalibrated)

df2<-cSplit(indt=res_comparison,splitCols = c("sensor"),sep="-",direction="wide",drop=FALSE)

df2 %>%
  select(-sensor_2,-sensor_3)->res_comparison



```

```{r}

res_comparison$sensor_1 <- factor(res_comparison$sensor_1, levels = c("PMS5003", "SPS030", "HPMA115S0", "SDS018", "OPCR1"))

res_comparison$status <- factor(res_comparison$status, levels = c("Not Calibrated", "Calibrated"))
res_comparison %>%
  filter(Variation == "Peak") %>%
  ggplot(aes(x=Experiment,y=ratio,fill=status)) + 
   geom_boxplot(width=0.8,position=position_dodge(width = 0.8),alpha=0.8,lwd=0.3,fatten=0.8,outlier.size = 0.8,outlier.alpha=0.5,outlier.stroke=0)+
  facet_wrap(sensor_1~Source,ncol=4)+ylab("Ratio sensor/dusttrak")+theme_bw()+
  scale_y_continuous(breaks=pretty(c(-1,6), n=14),sec.axis = dup_axis(name=NULL))+
  theme(axis.text.x = element_text(angle=90,size = 6))->p
p_legend<-get_legend(p)

plot_grid(p_legend)

p<-p+theme(legend.position = "none")
p<-p+  theme(panel.spacing = unit(0, "lines")) +
  theme(plot.margin = unit(c(0,0,0,0), "lines"))
p
#ggsave(p,filename = "ratio_comparison_spacing.svg",unit = "mm", width= 180, height = 240)
#(as_ggplot(p_legend),filename = "ratio_comparison_legend.svg",unit = "mm", width= 180, height = 240)


```




